
"""simple docstring"""




import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def     __magic_name__      ( __snake_case							:			Tuple	,					__snake_case							:			Dict	,					__snake_case							:			Optional[Any]    )					->							Optional[Any]:
       lowercase       :							Dict									=				LxmertConfig.from_json_file(UpperCamelCase_    )
       print(f"""Building PyTorch model from configuration: {config}"""    )
       lowercase       :							Any									=				LxmertForPreTraining(UpperCamelCase_    )

       # Load weights from tf checkpoint
       load_tf_weights_in_lxmert(UpperCamelCase_	,					UpperCamelCase_	,					UpperCamelCase_    )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
       torch.save(model.state_dict()	,					UpperCamelCase_    )


if __name__ == "__main__":
   _A     :			str  						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--config_file""",
       default=None,
       type=str,
       required=True,
       help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   _A     :			Tuple  						=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






def      UpperCAmelCase__		(UpperCamelCase_ = 10_00							):



     """simple docstring"""






     return sum(2 * a * ((a - 1) // 2) for a in range(3				,n + 1							)							)


if __name__ == "__main__":
 print(solution())




